From 3647911c3caf8a394dd1c536674c681652554030 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 25 Sep 2024 14:09:15 -0400 Subject: [PATCH] vault backup: 2024-09-25 14:09:15 --- 4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md index d2776e59..ceb7d0a0 100644 --- a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md +++ b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md @@ -17,7 +17,11 @@ The goal of this research is to use a generative diffusion model to create unstr # Version 1 ## Attempt -The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. If this research is successful, this diffusion model will accomplish three main tasks: +The goal of this research is to use a generative diffusion model to create unstructured perturbations of a nominal plant. In the real world, there is always a perturbation between the dynamics of the physical process and the mathematical model. Stability and performance of the controller suffer when this difference is large, but knowning that it is never zero, understanding how much performance is affected by the perturbation is important to know. Robust control answers this problem for mathematical models of plants. We can know precisely how much a model of a controller will be affected by a perturbation, and we can define a set of allowable perturbations that fit within our engineering specifications. + +A problem arises when we try + +If this research is successful, this diffusion model will accomplish three main tasks: 1. It will approximate a set of controllable plants by generating a large number of perturbed examples 2. Perturb a nominal plant in an unstructured manner with a controllable amount of uncertainty 3. Generate time and frequency domain responses based on training data of example systems.